Abstract 6176: Identifying poor prognosis stage I lung adenocarcinoma patients through novel morphological biomarker based on computational pathology
Notice bibliographique
Résumé
Abstract Current practice for evaluating prognosis of resectable lung adenocarcinoma (LUAD) patients relies on the Tumor, Node, Metastasis staging system. Despite low relapse risk, post-recurrence survival in a stage I population is poor with a median post relapse survival of around 25 months. There is a need for a biomarker that can enrich for stage I patients with relapse risk to identify a population likely to benefit from adjuvant therapy. In this study, we aim to identify a predictive biomarker of relapse in early-stage lung cancer following surgical resection using AI-based computational pathology. The retrospective patient cohort analyzed in this study consisted of a total of 166 patients that underwent surgical resection for a clinical stage I LUAD. Out of these, 54 patients experienced disease recurrence within 5 years, and 112 patients had a confirmed disease-free period of at least 5 years post-surgery. For each patient, at least one digitized slide (average = 2.46) stained with haematoxylin and eosin (H&E) was available for analysis. The project was accepted by the IUCPQ ethics committee (2022-3751, 22138). Manual annotations delineating the tumor core (TC) were drawn by pathologists. We then applied several proprietary image analysis models capable of distinguishing epithelium, stroma, and necrosis within the tumor tissue as well as detecting tumor infiltrating lymphocytes (TILs) and cell nuclei. In this project, we additionally introduce a novel approach rooted in mathematical graph theory to encoding topological aspects of the tissue morphology. A cell-graph constructed from the positions of cell nuclei was used to calculate cell-level graph-theoretic measures which were then aggregated to the slide-level by computing the median. In total, 23 data readouts were obtained of which 14 were based on the standard image analysis pipeline alone, and a further 9 originated from our newly developed graph-based approach. We identified promising biomarkers using the Wilcoxon rank-sum test and the area under the ROC curve (AUROC); robustness of this evaluation was investigating using resampling methods, using 100 bootstrap samples and 100 repeats of 3-fold cross-validation, respectively. This allowed us to evaluate the prognostic value of the biomarkers with respect to 5-year relapse or death. We were able to identify the best prognostic biomarker for relapse to be the median weighted clustering coefficient (CC) across all cells in the TC area, which is derived from the graph-based analyses. The median CC was able to achieve a whole-cohort AUROC of 0.695 (average cross-validated AUROC = 0.675, 95% interval 61.7%-70.4%) as well as a relapse vs relapse-free Wilcoxon test p-value of 4.65x10-5 (bootstrapped average = 0.0002). In addition, we were also able to demonstrate that the biomarker shows some relation to the architecture patterns. Citation Format: Florian J. Song, Alma Andoni, Manal Kordahi, Sara Batelli, Armin Meier, Markus Schick, Emilie Mahieu, Günter Schmidt, Claire E. Myers, Michael Abadier, Abjihit Dasgupta, Christopher Abbosh, Darren Hodgson, Michèle Orain, Fabien C. Lamaze, Yohan Bossé, Philippe Joubert. Identifying poor prognosis stage I lung adenocarcinoma patients through novel morphological biomarker based on computational pathology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6176.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».